How long does it take to set up Xplainr?

Most teams are up and running in under 5 minutes. Simply connect your marketing channels using OAuth (no API keys needed), and you'll see your first AI-generated insight immediately. There's no complex configuration or data mapping required.

Absolutely. We use bank-level 256-bit AES encryption for all data in transit and at rest. We never store your raw marketing data, only the aggregated metrics needed to generate insights. Our platform is built with enterprise-grade security practices, and we never sell or share your data.

Which marketing platforms do you integrate with?

We currently support Google Analytics 4, Google Ads, Google Search Console, Meta Ads (Facebook & Instagram), and LinkedIn Ads. All integrations use secure OAuth authentication, so you never have to share passwords or API keys.

Can I try Xplainr before committing?

Yes! Start with our Free tier to connect one domain and get 5 AI insights per month, no credit card required.

What if Xplainr doesn't work for me?

No problem - all paid plans come with a 14-day free trial, and you can cancel anytime with one click. If you're not seeing value within the first two weeks, you won't be charged. We're confident you'll see ROI, but there's zero risk to try.

How is Xplainr different from Google Analytics or Looker Studio?

Those tools show charts and expect you to figure them out. Xplainr tells you what happened in plain English, explains why it happened, and recommends what to do next. Instead of building dashboards and manually hunting for patterns, you get AI-generated insights that explain anomalies, identify opportunities, and prioritize your daily actions.

Can my whole marketing team use Xplainr?

Yes! Starter plans include 3 team members, Pro plans include 10, and Enterprise plans have unlimited seats. Team members can share domains, assign action items from insights, and collaborate on priorities - all with role-based permissions.

Do I need technical skills to use Xplainr?

Not at all. Xplainr is built for marketing managers, not data scientists. You can ask Aria (our AI assistant) questions in plain English like 'Why did my conversion rate drop yesterday?' and get clear, actionable answers without writing any queries.

Comparison Guide

Marketing Qualified Lead (MQL) vs Sales Qualified Lead (SQL)

MQLs and SQLs represent different stages in the buyer journey. MQLs show marketing interest; SQLs show buying intent. The handoff between them is where marketing and sales alignment succeeds or fails.

Quick Definitions

Marketing Qualified Lead (MQL)

An MQL is a lead that has shown interest through marketing interactions and meets marketing's criteria for sales-readiness based on behavior and demographics.

Sales Qualified Lead (SQL)

An SQL is a lead that has been vetted by sales and confirmed to have budget, authority, need, and timeline (BANT) or similar qualification criteria.

Side-by-Side Comparison

Aspect	MQL	SQL
Who qualifies	Marketing team	Sales team
Criteria focus	Engagement, demographics	Budget, authority, need, timeline
Funnel stage	Middle of funnel	Bottom of funnel
Typical volume	High (100s-1000s/month)	Low (10s-100s/month)
Conversion rate	5-20% to SQL	20-40% to opportunity
Primary metric for	Marketing performance	Sales pipeline
Time to close	Weeks to months	Days to weeks

Pros & Cons

Marketing Qualified Lead (MQL)

Advantages

Easier to generate at volume
Marketing can fully control
Good for top-of-funnel tracking
Helps measure content effectiveness

Limitations

Quality can vary significantly
May not indicate purchase intent
Can create sales friction
Quantity focus can hurt quality

Sales Qualified Lead (SQL)

Advantages

Higher conversion probability
Sales-verified quality
Better revenue predictor
Reduces sales time wasted

Limitations

Lower volume than MQLs
Depends on sales capacity
Subjective qualification
Can miss good opportunities

When to Use Each

Use MQL when...

You need to measure marketing's contribution to pipeline. You're optimizing content and campaigns. You need volume metrics for marketing ROI calculations.

Best for:

Content marketing: Measuring engagement
Lead generation: Top-of-funnel volume
Marketing reporting: Team-level metrics

Use SQL when...

You need to forecast revenue accurately. You're measuring sales efficiency. You want to reduce time sales spends on unqualified leads.

Best for:

Pipeline forecasting: Revenue prediction
Sales alignment: Shared metrics
Enterprise sales: High-value deals

How They Work Together

MQLs feed SQLs. Marketing generates MQLs through content and campaigns, then nurtures them until they're ready for sales. Sales qualifies promising MQLs into SQLs. The MQL-to-SQL conversion rate is a key metric for alignment - too low means marketing is sending junk, too high means marketing is too conservative.

How Xplainr Handles This

Xplainr tracks leads through both MQL and SQL stages, automatically calculating conversion rates and identifying where leads get stuck. Our AI spots when MQL quality is declining before it impacts SQL volume.

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Related Resources

Lead Quality vs Quantity Shared KPIs Guide Closed-Loop Reporting B2B Marketing Analytics

Frequently Asked Questions

What's a good MQL to SQL conversion rate?

Typically 10-25% is healthy. Below 10% suggests MQL criteria are too loose or sales isn't following up. Above 30% might mean marketing is too conservative and missing opportunities.

Who should own the MQL definition?

Marketing and sales should co-create MQL criteria. Marketing understands engagement signals; sales knows what predicts buying. Review and refine criteria quarterly.

Should we skip MQLs and go straight to SQLs?

Some companies are moving to a single "qualified lead" model, but MQLs remain valuable for measuring marketing effectiveness separately from sales capacity. The key is alignment on definitions.